{"id":18800,"date":"2023-02-03T05:14:55","date_gmt":"2023-02-03T05:14:55","guid":{"rendered":"https:\/\/file.currentschoolnews.com\/?post_type=product&p=18800"},"modified":"2023-02-03T09:58:06","modified_gmt":"2023-02-03T09:58:06","slug":"a-review-of-dimensionality-reduction-methods-and-their-applications","status":"publish","type":"product","link":"https:\/\/pastexamquestions.com\/product\/a-review-of-dimensionality-reduction-methods-and-their-applications\/","title":{"rendered":"A Review of Dimensionality Reduction Methods and their Applications"},"content":{"rendered":"

Download A Review of Dimensionality Reduction Methods and their Applications<\/strong><\/span>.<\/strong> Computer Science students who are writing their projects can get this material to aid their research work.<\/span><\/p>\n

Abstract<\/b><\/h2>\n

In the world we live in today, the reduction in data generally has seen a great rise. This is because of the numerous advantages that comes with working with smaller efficient data instead of the original large dataset.<\/p>\n

With this analogy, we can adopt Dimensionality reduction in computer science emphasizing on reducing computer memory in order to have more storage capacity on a computer. An example of this would be to reduce digital images which are then stored in 2D matrices.<\/p>\n

Dimensionality reduction is a process where by given a collection of data points in a high dimensional Euclidean space, it is often helpful to be able to project it into a lower dimensional Euclidean space without suffering great distortion.<\/p>\n

The result obtained by working in the lower dimensional space becomes a good approximation to the original dataset obtained by working in the high dimensional space.<\/p>\n

Dimensionality Reduction has two categories: In the first category includes those in which each attribute in the reduced set is a linear combination of the attributes in the original dataset. These include RP <\/em>and PCA<\/em>.<\/p>\n

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Introduction<\/span><\/span><\/strong><\/h2>\n

This project is mainly a survey on dimensionality reduction discussing different motives why we might want to reduce the dimensionality of a dataset. Outlining various works done, methods used and finally their applications in different domains of life.<\/p>\n

This project goes further in depth to look at different dimensionality reduction methods and ways in which we can implement a few of them.<\/p>\n

Finally, this project goes further to compare these techniques to the extent in which they preserve images and outlines the various applications in random projection.<\/p>\n

Assume a data set D contains n points in a high dimensional space, this can be mapped out onto a lower dimensional space with minimal distortion. (see Nsang, Novel Approaches to Dimensionality Reduction and Applications<\/em>). For example, a data set with 30,000 columns will be difficult to inspect.<\/p>\n

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